Article ; Online: Form follows function: Nuclear morphology as a quantifiable predictor of cellular senescence.
2023 Volume 22, Issue 12, Page(s) e14012
Abstract: Enlarged or irregularly shaped nuclei are frequently observed in tissue cells undergoing senescence. However, it remained unclear whether this peculiar morphology is a cause or a consequence of senescence and how informative it is in distinguishing ... ...
Abstract | Enlarged or irregularly shaped nuclei are frequently observed in tissue cells undergoing senescence. However, it remained unclear whether this peculiar morphology is a cause or a consequence of senescence and how informative it is in distinguishing between proliferative and senescent cells. Recent research reveals that nuclear morphology can act as a predictive biomarker of senescence, suggesting an active role for the nucleus in driving senescence phenotypes. By employing deep learning algorithms to analyze nuclear morphology, accurate classification of cells as proliferative or senescent is achievable across various cell types and species both in vitro and in vivo. This quantitative imaging-based approach can be employed to establish links between senescence burden and clinical data, aiding in the understanding of age-related diseases, as well as assisting in disease prognosis and treatment response. |
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MeSH term(s) | Cellular Senescence/physiology ; Phenotype ; Biomarkers/metabolism |
Chemical Substances | Biomarkers |
Language | English |
Publishing date | 2023-10-16 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 2113083-8 |
ISSN | 1474-9726 ; 1474-9718 |
ISSN (online) | 1474-9726 |
ISSN | 1474-9718 |
DOI | 10.1111/acel.14012 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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